Overview

Dataset statistics

Number of variables12
Number of observations268
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.2 KiB
Average record size in memory96.5 B

Variable types

NUM9
UNSUPPORTED1
CAT1
BOOL1

Reproduction

Analysis started2020-08-18 16:10:51.374240
Analysis finished2020-08-18 16:11:29.258172
Duration37.88 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Outcome has constant value "1" Constant
winner is an unsupported type, check if it needs cleaning or further analysis Unsupported
Pregnancies has 38 (14.2%) zeros Zeros
Insulin has 138 (51.5%) zeros Zeros

Variables

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct count17
Unique (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.865671641791045
Minimum0
Maximum17
Zeros38
Zeros (%)14.2%
Memory size2.1 KiB
2020-08-18T21:41:29.386368image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median4
Q38
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation3.741239044
Coefficient of variation (CV)0.7689049569
Kurtosis-0.441643254
Mean4.865671642
Median Absolute Deviation (MAD)3
Skewness0.5037492166
Sum1304
Variance13.99686958
2020-08-18T21:41:29.636625image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03814.2%
 
12910.8%
 
32710.1%
 
7259.3%
 
4238.6%
 
8228.2%
 
5217.8%
 
2197.1%
 
9186.7%
 
6166.0%
 
Other values (7)3011.2%
 
ValueCountFrequency (%) 
03814.2%
 
12910.8%
 
2197.1%
 
32710.1%
 
4238.6%
 
ValueCountFrequency (%) 
1710.4%
 
1510.4%
 
1420.7%
 
1351.9%
 
1241.5%
 

Glucose
Real number (ℝ≥0)

Distinct count103
Unique (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.30223880597015
Minimum78
Maximum199
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:29.919831image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile98.05
Q1119
median140
Q3167
95-th percentile189.65
Maximum199
Range121
Interquartile range (IQR)48

Descriptive statistics

Standard deviation29.48881072
Coefficient of variation (CV)0.2072266112
Kurtosis-0.9424872734
Mean142.3022388
Median Absolute Deviation (MAD)23
Skewness0.09273334353
Sum38137
Variance869.5899575
2020-08-18T21:41:30.178982image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12572.6%
 
15862.2%
 
11562.2%
 
12962.2%
 
12862.2%
 
17351.9%
 
14051.9%
 
18151.9%
 
10951.9%
 
14651.9%
 
Other values (93)21279.1%
 
ValueCountFrequency (%) 
7810.4%
 
8010.4%
 
8410.4%
 
8510.4%
 
8810.4%
 
ValueCountFrequency (%) 
19910.4%
 
19810.4%
 
19731.1%
 
19631.1%
 
19520.7%
 

BloodPressure
Real number (ℝ≥0)

Distinct count38
Unique (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.24253731343283
Minimum30
Maximum114
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:30.435213image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile56.7
Q168
median74
Q382
95-th percentile94
Maximum114
Range84
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.92976247
Coefficient of variation (CV)0.1585507732
Kurtosis1.281644254
Mean75.24253731
Median Absolute Deviation (MAD)8
Skewness0.09964127194
Sum20165
Variance142.3192325
2020-08-18T21:41:30.641763image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
743312.3%
 
70238.6%
 
76186.7%
 
78176.3%
 
72166.0%
 
64134.9%
 
80134.9%
 
82134.9%
 
84124.5%
 
68124.5%
 
Other values (28)9836.6%
 
ValueCountFrequency (%) 
3010.4%
 
4010.4%
 
4810.4%
 
5051.9%
 
5231.1%
 
ValueCountFrequency (%) 
11410.4%
 
11020.7%
 
10810.4%
 
10610.4%
 
10420.7%
 

SkinThickness
Real number (ℝ≥0)

Distinct count42
Unique (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.029850746268657
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:30.887623image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile19
Q127
median27
Q336
95-th percentile46
Maximum99
Range92
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.914861806
Coefficient of variation (CV)0.2872995387
Kurtosis12.42011205
Mean31.02985075
Median Absolute Deviation (MAD)4
Skewness2.059665099
Sum8316
Variance79.47476103
2020-08-18T21:41:31.156573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
279535.4%
 
32145.2%
 
3093.4%
 
3393.4%
 
3983.0%
 
3683.0%
 
3783.0%
 
3583.0%
 
2972.6%
 
4172.6%
 
Other values (32)9535.4%
 
ValueCountFrequency (%) 
710.4%
 
1210.4%
 
1310.4%
 
1420.7%
 
1510.4%
 
ValueCountFrequency (%) 
9910.4%
 
6310.4%
 
5610.4%
 
5110.4%
 
4931.1%
 

Insulin
Real number (ℝ≥0)

ZEROS

Distinct count93
Unique (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.33582089552239
Minimum0
Maximum846
Zeros138
Zeros (%)51.5%
Memory size2.1 KiB
2020-08-18T21:41:31.426771image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3167.25
95-th percentile366.5
Maximum846
Range846
Interquartile range (IQR)167.25

Descriptive statistics

Standard deviation138.6891247
Coefficient of variation (CV)1.382249365
Kurtosis4.360492769
Mean100.3358209
Median Absolute Deviation (MAD)0
Skewness1.843831487
Sum26890
Variance19234.67332
2020-08-18T21:41:31.673296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
013851.5%
 
13062.2%
 
18041.5%
 
15631.1%
 
17531.1%
 
19420.7%
 
12520.7%
 
22520.7%
 
22020.7%
 
21020.7%
 
Other values (83)10438.8%
 
ValueCountFrequency (%) 
013851.5%
 
1410.4%
 
2910.4%
 
3610.4%
 
4810.4%
 
ValueCountFrequency (%) 
84610.4%
 
60010.4%
 
57910.4%
 
54310.4%
 
54010.4%
 

BMI
Real number (ℝ≥0)

Distinct count148
Unique (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.39813432835821
Minimum22.9
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:31.944255image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum22.9
5-th percentile26.535
Q130.9
median34.275
Q338.775
95-th percentile46.59
Maximum67.1
Range44.2
Interquartile range (IQR)7.875

Descriptive statistics

Standard deviation6.590915342
Coefficient of variation (CV)0.1861938621
Kurtosis2.101668987
Mean35.39813433
Median Absolute Deviation (MAD)3.775
Skewness1.027526692
Sum9486.7
Variance43.44016505
2020-08-18T21:41:32.164141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
32.983.0%
 
31.672.6%
 
33.362.2%
 
30.551.9%
 
3251.9%
 
31.251.9%
 
32.441.5%
 
30.441.5%
 
43.341.5%
 
34.341.5%
 
Other values (138)21680.6%
 
ValueCountFrequency (%) 
22.910.4%
 
23.310.4%
 
23.410.4%
 
23.510.4%
 
23.810.4%
 
ValueCountFrequency (%) 
67.110.4%
 
59.410.4%
 
5510.4%
 
53.210.4%
 
52.910.4%
 

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct count231
Unique (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5505
Minimum0.08800000000000001
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:32.433046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.088
5-th percentile0.1517
Q10.2625
median0.449
Q30.728
95-th percentile1.224
Maximum2.42
Range2.332
Interquartile range (IQR)0.4655

Descriptive statistics

Standard deviation0.3723544836
Coefficient of variation (CV)0.676393249
Kurtosis4.559082526
Mean0.5505
Median Absolute Deviation (MAD)0.206
Skewness1.722373119
Sum147.534
Variance0.1386478614
2020-08-18T21:41:32.655419image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.25441.5%
 
0.25831.1%
 
0.58320.7%
 
0.30220.7%
 
0.23820.7%
 
0.2420.7%
 
0.2620.7%
 
0.2720.7%
 
0.25920.7%
 
0.32820.7%
 
Other values (221)24591.4%
 
ValueCountFrequency (%) 
0.08810.4%
 
0.12110.4%
 
0.12720.7%
 
0.12810.4%
 
0.12920.7%
 
ValueCountFrequency (%) 
2.4210.4%
 
2.28810.4%
 
2.13710.4%
 
1.89310.4%
 
1.39410.4%
 

Age
Real number (ℝ≥0)

Distinct count45
Unique (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.06716417910448
Minimum21
Maximum70
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:33.134203image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile22
Q128
median36
Q344
95-th percentile57.65
Maximum70
Range49
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.96825365
Coefficient of variation (CV)0.2959021521
Kurtosis-0.3479372743
Mean37.06716418
Median Absolute Deviation (MAD)8
Skewness0.5816458715
Sum9934
Variance120.3025882
2020-08-18T21:41:33.342407image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25145.2%
 
31134.9%
 
29134.9%
 
41134.9%
 
43114.1%
 
22114.1%
 
38103.7%
 
28103.7%
 
36103.7%
 
33103.7%
 
Other values (35)15357.1%
 
ValueCountFrequency (%) 
2151.9%
 
22114.1%
 
2372.6%
 
2483.0%
 
25145.2%
 
ValueCountFrequency (%) 
7010.4%
 
6710.4%
 
6620.7%
 
6220.7%
 
6110.4%
 

Outcome
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
1
268
ValueCountFrequency (%) 
1268100.0%
 

winner
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size2.2 KiB

weights
Real number (ℝ)

Distinct count150
Unique (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.554585820895502
Minimum-18.83249999999996
Maximum-0.1340000000000001
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-18T21:41:33.776933image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-18.8325
5-th percentile-17.001325
Q1-13.018125
median-9.428
Q3-5.976
95-th percentile-2.719725
Maximum-0.134
Range18.6985
Interquartile range (IQR)7.042125

Descriptive statistics

Standard deviation4.421485435
Coefficient of variation (CV)-0.4627605548
Kurtosis-0.8368224386
Mean-9.554585821
Median Absolute Deviation (MAD)3.50725
Skewness-0.05805241807
Sum-2560.629
Variance19.54953346
2020-08-18T21:41:34.018072image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-9.29551.9%
 
-18.23551.9%
 
-4.670541.5%
 
-6.794541.5%
 
-4.07341.5%
 
-4.91441.5%
 
-12.52641.5%
 
-11.19841.5%
 
-7.989541.5%
 
-6.573531.1%
 
Other values (140)22784.7%
 
ValueCountFrequency (%) 
-18.832520.7%
 
-18.23551.9%
 
-17.88110.4%
 
-17.637510.4%
 
-17.283520.7%
 
ValueCountFrequency (%) 
-0.13420.7%
 
-0.731510.4%
 
-1.085520.7%
 
-1.32910.4%
 
-1.68310.4%
 
Distinct count4
Unique (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
HIGH
82
MODERATE
72
ALARMING
70
LOW
44
ValueCountFrequency (%) 
HIGH8230.6%
 
MODERATE7226.9%
 
ALARMING7026.1%
 
LOW4416.4%
 
2020-08-18T21:41:34.494694image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length5.955223881
Min length3

Interactions

2020-08-18T21:41:04.596012image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:05.536430image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:06.016644image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:06.368827image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:06.893271image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:07.399254image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:07.998321image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:08.535626image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:08.992369image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:09.527942image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:10.045549image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:10.319750image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:10.715896image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:10.981947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:11.247451image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:11.440476image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:11.738369image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:12.046665image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:12.344754image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:12.618242image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:12.861933image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:13.092253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:13.308643image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:13.447190image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:13.631292image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:13.872678image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:14.088806image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:14.327852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:14.584933image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:14.833555image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:15.070592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:15.305295image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:15.540983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:15.771094image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:16.025685image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:16.251298image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:16.514407image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:16.995649image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:17.246939image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:17.481784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:17.740277image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:18.028429image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:18.241110image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:18.475692image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:18.710523image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:18.966550image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:19.217252image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:19.449210image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:19.667792image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:19.892058image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:20.115038image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:20.325899image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:20.556880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:20.785378image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:21.042336image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:21.320729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:21.587528image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:21.836536image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:22.093721image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:22.350482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:22.592422image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:22.897022image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:23.168541image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:23.556676image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:23.838543image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:24.090033image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:24.322949image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:24.566329image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:24.879098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:25.102592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:25.359160image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:25.595494image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:25.851150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:26.139452image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:26.404207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:26.668473image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:26.890225image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:27.071352image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:27.225384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:27.461993image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:27.736134image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-18T21:41:34.844181image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:35.338474image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:35.846771image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:36.297782image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Missing values

2020-08-18T21:41:28.297381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-18T21:41:28.971287image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcomewinnerweightsSeverity of diabetes
061487235033.600.627501(2, 5)-5.0245ALARMING
181836427023.300.672321(0, 3)-1.9265ALARMING
20137403516843.102.288331(12, 0)-11.5520MODERATE
337850328831.000.248261(12, 9)-16.9295LOW
42197704554330.500.158531(14, 4)-15.8450LOW
581259627034.250.232541(5, 3)-6.6840HIGH
6101687427038.000.537341(4, 5)-6.9275HIGH
71189602384630.100.398591(13, 4)-14.8935LOW
85166721917525.800.587511(0, 2)-1.3290ALARMING
971007427030.000.484321(7, 4)-9.1845HIGH

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcomewinnerweightsSeverity of diabetes
2583187702220036.40.408361(14, 6)-17.0400LOW
25961626227024.30.178501(0, 5)-3.1215ALARMING
26041367027031.21.182221(11, 8)-15.3805LOW
2610181884451043.30.222261(11, 5)-13.5880MODERATE
26281547832032.40.443451(2, 5)-5.0245ALARMING
2631128883911036.51.057371(8, 6)-11.3310MODERATE
26401237227036.30.258521(8, 3)-9.5385HIGH
26561909227035.50.278661(0, 7)-4.3165ALARMING
26691707431044.00.403431(2, 4)-4.4270ALARMING
26711266027030.10.349471(11, 7)-14.7830LOW